{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    0列为pregnants(怀孕次数)； \n",
    "    1列为Plasma_glucose_concentration(口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度)；\n",
    "    2列为blood_pressure(舒张压,单位:mm Hg） \n",
    "    3列为Triceps_skin_fold_thickness(三头肌皮褶厚度,单位：mm） \n",
    "    4列为serum_insulin(餐后血清胰岛素,单位:mm） \n",
    "    5列为BMI,体重指数（体重（公斤）/ 身高（米）^2）\n",
    "    6列为Diabetes_pedigree_function(糖尿病家系作用)\n",
    "    7列为Age(年龄) \n",
    "    8列为Target(分类变量,0或1）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#首先 import 必要的模块\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape:  (768, 9)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()\n",
    "print(\"train shape: \", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集已知存在缺失值，某些列中存在的缺失值被标记为0。通过这些列中指标的定义和相应领域的常识可以证实上述观点，譬如体重指数和血压两列中的0作为指标数值来说是无意义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果中，很多列的最小值是0 ，这个不符合常理，根据说明得知，0代表缺失值\n",
    "下列变量的最小值为0时数据无意义： 1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第1、2、5列中0值较少；相比较而言，第3、4列中的0值多出数倍，接近总量的一半。\n",
    "为了确保有足够的数据量来训练模型，针对不同的列需要有不同的缺失值判断策略。插入均值，或者标记是否是缺失值的列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看每个变量的分布 及其与标签之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 各个统计直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x168589dada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16858c81b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16858c02390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a1011d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16858be81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16858ca0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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9tftT9Q+SvGHflLbfaP0cGNRBcw4kWQ28Gdg2a9VBcy4scAxgieeC89EfGO4BVlXV80nO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685900ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a311c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VfZ3kkQpJfqfb/3cMvoHxSuAg8F0GK4IFq+ec3w48A7i2W+EeqwX8QKiec25KnzlX1Z1J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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a1655c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#该问题为分类问题，类别型特征直方图可用countplot\n",
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "医疗类数据中，一些数据值分布比较广，一般代表着这个数据对应的人不正常，和有可能跟病发事件有关系，比如怀孕17次的那个，三头肌皮褶厚度那个数值大的数据，异常值可能就意味着得病，不能删除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 怀孕次数pregnants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1685a360f28>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a3b9e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数和得病之间的关系\n",
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中看出，怀孕次数越多，得病的机会越多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plasma_glucose_concentration与target的关系\n",
    "血浆葡萄糖浓度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k4Be2GGkKwtxtWRx9zLF4vYnpv3GSLHKBqj32VQPZsQsnOW3cuJGHJ0wg2K0X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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168589d6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个数值也和发病率有关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### blood_pressure与target的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Uvr06RO167alprFdRAYDZwCfq2otEA90qmnSKSkpJCQm4qre5VwQbt9eHWy4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\nANOrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a22eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='blood_pressure', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个看不出来有什么"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Triceps_skin_fold_thickness与target之间的关系\n",
    "三头肌皮褶厚度（单位：mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8nsD64kjWF0cyZkEKU34Pzo/EtFjl1gMLKSoqYPSoUbhcwZ034usM7nNrOR6o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\n4vAMQzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a075f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看出发病率的那个上面长长的头部代表着异常数据，和发病率有直接相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### serum_insulin 与 target之间的关系\n",
    "餐后血清胰岛素（单位:mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ppzJhwgSb38mktJ07d/L000+zdMkSfH4fXyhqYWqph2MGeJOmpTiU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\nLFkYY4wJy5KFMcaYsCxZGGOMCev/A8Uxx+mQHLRFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16858c56d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BMI\n",
    "体重指数（体重（公斤）/ 身高（米）^2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VfwO/B7oC7wCo6iwaJrbtkzao8D+M9H8pcBHQuODedODi/ar+AFVVVbF58ya8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\nxpgmFgrGGGOa/H9zh6Q2chl9fAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16858f95198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "越胖的人，发病率越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16858c1f7b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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24njsTYydX2/ZI06rc966xzuvll8VVbeH/P9GaOb/G1T67kjj+I/SuO9FTQV1M5tICOjX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\nUBcRKREFdRGRElFQFxEpEQV1EZES+U/aZLX3QCAeMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168589da358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个图也可以发现，BMI越大，发病率越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diabetes_pedigree_function，糖尿病家系作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16858f1c358>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XZMfj+/pbM3rHbwztjx/hxkXRXvzN5kC8F9hIvHfQZryH0Ok0vHgeDG3xK+AK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\neAshRA2R8RZCiBoi4y2EEDXkfwCANf0bMPo2LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a2ee9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Diabetes_pedigree_function', 'Target'])['Diabetes_pedigree_function'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1685ab9f4a8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAENCAYAAAAfTp5aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAG7BJREFUeJzt3X2UVdV5x/HvIy8BAwUHxwlxxCFRiYNECCOUxhgVX4g2\nQBrLkq4mUIlktUpimlhJmyqxsSVtV2IaGteymjomvsYkQkxiRKJxxVXDm2BAJEQFGQoymRIib1Hx\n6R9nD1xu7p37/jJ7fp+17rrn7HP32Xufe+eZfffZ51xzd0REpPc7rtYVEBGR8lBAFxGJhAK6iEgk\nFNBFRCKhgC4iEgkFdBGRSCigi4hEQgFdRCQSCugiIpFQQBcRiUT/ahZ24oknektLSzWLFBHp9das\nWfMbd2/M9bqqBvSWlhZWr15dzSJFRHo9M9uWz+s05CIiEgkFdBGRSCigi4hEoqpj6CIitfDGG2/Q\n0dHBoUOHal2VHg0aNIjm5mYGDBhQVH4FdBGJXkdHB0OHDqWlpQUzq3V1MnJ3urq66OjoYPTo0UXt\nQ0MuIhK9Q4cOMWLEiLoN5gBmxogRI0r6FqGALiJ9Qj0H826l1lFDLiLSJ3V1dTF16lQAdu3aRb9+\n/WhsTK7dWblyJQMHDix7mWvXrmX37t1Mmzat7PuGegvoi4alLO+tXT1EJHojRoxg3bp1ACxatIgh\nQ4bwuc99Lu/8hw8fpl+/fgWVuXbtWjZs2FCxgK4hFxGRNB/+8IeZOHEiY8eO5Y477gDgzTffZPjw\n4Vx33XW8973vZeXKlSxbtowxY8YwceJEFixYwMyZMwHYt28fc+fOZdKkSUyYMIEf/OAHHDx4kJtv\nvpl77rmH8ePH89BDD5W93jl76GY2BnggJeldwI3A3SG9BdgKzHL3PWWvoYhIlbW3t9PQ0MCBAwdo\na2vjox/9KEOHDmXv3r2cd9553HrrrRw4cIAzzjiDp59+mlGjRjFr1qwj+W+++WamTZvGXXfdxZ49\ne5g8eTLPPfccN954Ixs2bODWW2+tSL1z9tDdfbO7j3f38cBE4ADwfWAhsMLdTwdWhHURkV7vq1/9\nKmeffTZTpkyho6ODF198EYCBAwfykY98BIDnn3+eMWPGcOqpp2JmzJ49+0j+xx57jFtuuYXx48dz\nwQUXcOjQIV555ZWK17vQMfSpwIvuvs3MZgDnh/R24EnghvJVTUSk+h5//HGeeuopnnnmGQYPHsy5\n5557ZCrh4MGD85qJ4u48/PDDvPvd7z4m/amnnqpInbsVOoZ+JXBfWG5y951heRfQVLZaiYjUyN69\ne2loaGDw4MFs3LiRVatWZXxda2srmzdvZvv27bg7DzxwdGT60ksv5etf//qR9WeffRaAoUOH8tpr\nr1Ws7nkHdDMbCEwHvpO+zd0d8Cz55pvZajNb3dnZWXRFRUSq4fLLL+fAgQO0trbyhS98gcmTJ2d8\n3fHHH8+SJUu46KKLaGtrY/jw4QwblszUu+mmm9i/fz/jxo1j7NixLFq0CIALL7yQ9evXM2HChIqc\nFLUkFufxwmSI5Rp3vySsbwbOd/edZjYSeNLdx/S0j7a2Nu/xfuiatigiFbBp0ybOPPPMsu933759\nDBkyBHfnk5/8JOPGjWPBggUl7TNTXc1sjbu35cpbyJDLbI4OtwAsA+aE5TnA0gL2JSLS6912222M\nHz+e1tZWDh48yNVXX13T+uR1UtTM3g5cDHwyJXkx8KCZzQO2AbMy5RURidX111/P9ddfX+tqHJFX\nQHf3/cCItLQuklkvIiJSB3SlqIhIJBTQRUQioYAuIhIJBXQRkSp59NFHGTNmDKeddhqLFy8u+/7r\n6/a5IiJV0LLwh2Xd39bFl+d8zeHDh7nmmmtYvnw5zc3NnHPOOUyfPp3W1tay1UM9dBGRKli5ciWn\nnXYa73rXuxg4cCBXXnklS5eW9/IdBXQRkSrYsWMHp5xyypH15uZmduzYUdYyFNBFRCKhgC4iUgUn\nn3wy27dvP7Le0dHBySefXNYyFNBFRKrgnHPOYcuWLbz88su8/vrr3H///UyfPr2sZWiWi4hIFfTv\n358lS5Zw6aWXcvjwYa666irGjh1b3jLKujcRkV4gn2mGlXDZZZdx2WWXVWz/GnIREYmEArqISCQU\n0EVEIlGbMXT91JyISNmphy4iEgkFdBGRSCigi4hUyVVXXcVJJ53EWWedVZH9ax66iPQ9qefxyrK/\n/M4Fzp07l2uvvZaPf/zj5S0/yKuHbmbDzewhM3vBzDaZ2RQzazCz5Wa2JTyfUJEaiohE4rzzzqOh\noaFi+893yOVrwKPu/h7gbGATsBBY4e6nAyvCeklaDt175CEiIoXJGdDNbBhwHnAngLu/7u6/BWYA\n7eFl7cDMSlVSRERyy6eHPhroBP7bzJ41szvM7O1Ak7vvDK/ZBTRlymxm881stZmt7uzsLE+tRUTk\nD+QT0PsD7wNuc/cJwH7Shlfc3QHPlNndb3f3Nndva2xsLLW+IiKSRT4BvQPocPdfhPWHSAL8q2Y2\nEiA8765MFUVE4jB79mymTJnC5s2baW5u5s477yzr/nNOW3T3XWa23czGuPtmYCrwfHjMARaH5/L+\n2qmISKXU6JYj9913X0X3n+889AXAPWY2EHgJ+CuS3v2DZjYP2AbMqkwVRUQkH3kFdHdfB7Rl2DS1\nvNUREZFi1eRK0dR55ltrUQERkQjpXi4i0ickk/HqW6l1VEAXkegNGjSIrq6uug7q7k5XVxeDBg0q\neh+6OZeIRK+5uZmOjg7q/eLGQYMG0dzcXHR+BXQRid6AAQMYPXp0ratRcRpyERGJhAK6iEgkFNBF\nRCKhgC4iEgkFdBGRSCigi4hEQgFdRCQSCugiIpFQQBcRiYQCuohIJBTQRUQioYAuIhIJBXQRkUgo\noIuIRCKv2+ea2VbgNeAw8Ka7t5lZA/AA0ELyS3Kz3H1PZaopIiK5FNJDv8Ddx7t7949FLwRWuPvp\nwIqwLiIiNVLKkMsMoD0stwMzS6+OiIgUK9+A7sDjZrbGzOaHtCZ33xmWdwFNZa+diIjkLd+foDvX\n3XeY2UnAcjN7IXWju7uZZfz11fAPYD7AqFGjSqqsiIhkl1cP3d13hOfdwPeBScCrZjYSIDzvzpL3\ndndvc/e2xsbG8tRaRET+QM6AbmZvN7Oh3cvAJcAGYBkwJ7xsDrC0UpUUEZHc8hlyaQK+b2bdr7/X\n3R81s1XAg2Y2D9gGzKpcNUVEJJecAd3dXwLOzpDeBUytRKVERKRwulJURCQS+c5y6X0WDUtb31ub\neoiIVIl66CIikVBAFxGJhAK6iEgkFNBFRCKhgC4iEoneP8sldTaLZrKISB+mHrqISCQU0EVEIqGA\nLiISCQV0EZFIKKCLiERCAV1EJBIK6CIikVBAFxGJhAK6iEgkFNBFRCKhgC4iEgkFdBGRSOQd0M2s\nn5k9a2aPhPUGM1tuZlvC8wmVq6aIiORSSA/908CmlPWFwAp3Px1YEdZFRKRG8groZtYMXA7ckZI8\nA2gPy+3AzPJWTURECpFvD/1W4O+At1LSmtx9Z1jeBTSVs2IiIlKYnAHdzP4U2O3ua7K9xt0d8Cz5\n55vZajNb3dnZWXxNRUSkR/n00N8PTDezrcD9wIVm9m3gVTMbCRCed2fK7O63u3ubu7c1NjaWqdoi\nIpIuZ0B398+7e7O7twBXAj91978ElgFzwsvmAEsrVksREcmplHnoi4GLzWwLcFFYFxGRGinoR6Ld\n/UngybDcBUwtf5VERKQYulJURCQSBfXQa2bRsLT1vUcWWw7de2R5a5WqIyJSj9RDFxGJhAK6iEgk\nFNBFRCKhgC4iEgkFdBGRSCigi4hEQgFdRCQSvWMeej1InQufMg9eRKReqIcuIhIJBXQRkUhEO+SS\neksA0G0BRCR+6qGLiERCAV1EJBLRDrn0SDNWRCRC6qGLiERCAV1EJBJ9c8glGw3FiEgvph66iEgk\ncgZ0MxtkZivNbL2ZbTSzL4b0BjNbbmZbwvMJla+uiIhkk8+Qy++BC919n5kNAH5uZj8G/gxY4e6L\nzWwhsBC4oRKV1EVCIiK55eyhe2JfWB0QHg7MANpDejswsyI1FBGRvOQ1hm5m/cxsHbAbWO7uvwCa\n3H1neMkuoKlCdRQRkTzkFdDd/bC7jweagUlmdlbadifptf8BM5tvZqvNbHVnZ2fJFRYRkcwKmuXi\n7r8FngCmAa+a2UiA8Lw7S57b3b3N3dsaGxtLra+IiGSRzyyXRjMbHpYHAxcDLwDLgDnhZXOApZWq\npIiI5JbPLJeRQLuZ9SP5B/Cguz9iZv8DPGhm84BtwKwK1rN+6WIkEakTOQO6uz8HTMiQ3gVMrUSl\nRESkcLpSVEQkEgroIiKRUEAXEYmEArqISCQU0EVEIqGALiISCQV0EZFIKKCLiERCAV1EJBJ98jdF\nU38wY2sF84iIVJN66CIikVBAFxGJRJ8ccsmmasMqukOjiFSAeugiIpFQQBcRiYQCuohIJBTQRUQi\noYAuIhIJzXKR6kud5QOa6SNSJuqhi4hEImdAN7NTzOwJM3vezDaa2adDeoOZLTezLeH5hMpXV0RE\nssmnh/4m8Fl3bwX+GLjGzFqBhcAKdz8dWBHWJdWiYUcfIiIVljOgu/tOd18bll8DNgEnAzOA9vCy\ndmBmpSopIiK5FTSGbmYtwATgF0CTu+8Mm3YBTWWtmYiIFCTvgG5mQ4DvAte5++9St7m7A54l33wz\nW21mqzs7O0uqrIiIZJdXQDezASTB/B53/15IftXMRobtI4HdmfK6++3u3ububY2NjeWos4iIZJBz\nHrqZGXAnsMndv5KyaRkwB1gcnpdWpIZSXboTpEivlc+FRe8HPgb80szWhbS/JwnkD5rZPGAbMKsy\nVRQRkXzkDOju/nPAsmyeWt7qiIhIsXTpv9QXDfmIFE2X/ouIREIBXUQkEhpy6S00FCEiOaiHLiIS\nCQV0EZFIaMilRC2H7j2yvLXUnVXrhx+KGb7RkI9I3VMPXUQkEgroIiKR0JBLBZV1OEYKp2Ei6WPU\nQxcRiYQCuohIJDTk0ttpWEFEAvXQRUQioYAuIhIJDbnIMSp2oVSpw0HlHlrSUJVESD10EZFIKKCL\niERCQy5Smmrdf0ZEcsrZQzezb5rZbjPbkJLWYGbLzWxLeD6hstUUEZFc8umh3wUsAe5OSVsIrHD3\nxWa2MKzfUP7qxUm3BMhOx0akeDl76O7+FPB/ackzgPaw3A7MLHO9RESkQMWeFG1y951heRfQVKb6\niIhIkUo+KerubmaebbuZzQfmA4waNarU4iRNj0MUmmst0qcU20N/1cxGAoTn3dle6O63u3ubu7c1\nNjYWWZyIiORSbEBfBswJy3OApeWpjoiIFCvnkIuZ3QecD5xoZh3ATcBi4EEzmwdsA2ZVspJSv1KH\nfCC/mSnF5BGR3HIGdHefnWXT1DLXRURESqBL/0VEIqFL/6X3q9fZPD3VK9u2em2L9ArqoYuIREIB\nXUQkEhpyqSM9zf7ojfc4qXWda11+dDQcVPfUQxcRiYQCuohIJDTk0gcVMxQR2/BF1vb0xmGF3lhn\nqQj10EVEIqGALiISCQ25RCy2YZKa6um3U6s15FGNcsr9G7EaDqoq9dBFRCKhgC4iEgkNuYjkoVfe\n8reI4ZO6uLgtyzBNy8IfHi1/8eXlKyOtnN5MPXQRkUiohy69Xr2e/O2pXtWoc4/fKsp5srKHfdX6\nvSmqV9+LT+Sqhy4iEgkFdBGRSGjIRXqFan117423Rah1+UWp8TBNuU/+lvWEbQnUQxcRiURJAd3M\nppnZZjP7tZktLFelRESkcEUPuZhZP+A/gYuBDmCVmS1z9+fLVTmRvqZaM2Nqva+ylp8y3AGVG/Io\n54yZnupcyvBNKT30ScCv3f0ld38duB+YUcL+RESkBKUE9JOB7SnrHSFNRERqwNy9uIxmVwDT3P0T\nYf1jwGR3vzbtdfOB+WF1DLA5LJ8I/CbL7rNtKzQ9tjy1Lr9aeWpdfjF5al1+tfLUuvxi8tS6/HLk\nOdXdG7O87ih3L+oBTAF+krL+eeDzBeRfXei2QtNjy1Pr8tXO+i1f7azf8sudp6dHKUMuq4DTzWy0\nmQ0ErgSWlbA/EREpQdGzXNz9TTO7FvgJ0A/4prtvLFvNRESkICVdKeruPwJ+VGT224vYVmh6bHlq\nXX618tS6/GLy1Lr8auWpdfnF5Kl1+eXOk1XRJ0VFRKS+6NJ/EZFIKKCLiERCAV1EJBJRBHQzO6mI\nPCMqUZdaKfQY9PX2hzzRHIO+3n7QMQCKv7CoHA/gj4AXgW8Bf5G27b+B20huADYCWAT8EngYOBNo\nCI8RJPf3OQG4IiX/MOBO4DlgI3BmSG8DXgJ+DWwDfgV8AXh3WvltwBPAt4FTgOXAXmANydnnjWG9\nE3gGmBvKXAy8APwf0AVsCmnDsxyDx4B/ST8GwDuADRna/2Ba+1OPwRVAQ4Xbvwr4E+DmDMfgr+u0\n/fcC/wGcmOEY/B64o0ztr9ZnoNZ/A33iM1BkHOip/adkamco5xtZjs2PC4qpVQrc78vyWAHsA2aS\nXJT0XeBtIc9eYAGwMLwhN4QD8hawH3g55fFGeP59Spl3AF8CTgX+F3g4pD8BnBOWzwhv5r8DrwAr\ngc8A7wzLHwJmk9yz5oqQ5+nwIWgG/hb4R+B0oD18QG4A3pH2ofwa8D8Z2j8ROBje7GOOAfBoKDe9\n/QsAT2t/9zH4PfBShds/NXxQ52Y4BjuAn9Zh+z8D7E2pU+ox2A7sKlP7q/UZqPXfQJ/4DIRthcaB\nbO2/AXg1SzvfRxL0Mx2bnfUY0A+HRj6R9ngNOJjyun8IB2oEcCAl/ZWU5c8CvwPGpaS9HJ7XpqSt\nS1ne1L0OPJNWt9TyPwB8I7y5rwHzM5S/Hng2ZX1VeD6OlD+mDO3fn6H9TwBvpb22+xg8192e1PLD\n+g6SD/sxx6Aa7U/Pk3YMNgMv1Fv7w/ohoH/6MQDWAr8sU/ur8hmgxn8Dfegz8ETacc8nDmRsf3f5\nWdp5OBzrTMfmYKZ9ZXtUK6BvAE7PkL4J2J6WNpfka8zrKWlfypDvO8BXgKEc/Y/cQfLf8rPhze2e\nZ78gHLALSb62fQ34IPBFoCtDvfqFMn4C/DnJ19KZKW15ISxP59j72ewH/g5oSklrCh+Op7McmzeA\n4zIcg0PAtiztf46kZ3DMMahS+z9I8q3q3PRjQPLVeXe9tT8lADyW4RjsBL5VjvZX+TNQy7+BvvIZ\nmBbackmGY5AxDvTQ/hvCZyNbO3dkOTbbM6Vne1QroF8BjMmQ/q/AjRnSp5GMPQ3JsO004KGUA/kM\nsCus35T2aAzp7wgfzAeAZ0nG4X5EchfIB7LU+eyQ58fAe8Kb/1uSr1nPA3uAn3e3C2gMb9qXScbP\n9pB8Ld1EMuY5KUs53wMuypB+D8n95rO2P/0YVKH9e0j+2c4h+SrafQzOSKnb4ynt31OF9s/Is/13\nAxdkOAargAEZyhmfR/t/W+vPAJX9G8jnGOT6DNTT38DdwPn5fgZy/B2kx4Fc7f8yyRh+pnZ+Cdia\npfyZBcXaQl5cyiMcjKmkBWngE1nSP5RPHmAwcFZIn1ZEOT3lubqH9Iuy7GsSR8fmxpL0FC5LS28l\n6UVcFtYzbisgzziSk1pZ82SrV47yJ/eQZ3KmPBne92+lp4X0u3v4rGTc1kP6YOA7VSgnY1ty5PlA\nOG6XpKWfG47ZJRnyZNzWQ/oHwvtfyL4y1qsMeRYAw0La8SQnTh8hCabNKe/XzcAPSALdhSl5Urel\n5/liWp4/SinnX0kC6d3AKWnlZ8tTbN0eyVBOd90eB1qzfBY+1Z0nLf1twMcJwR74C2AJcA1Z/tFk\ne1Tl0n8z+1So3CaS//qfdvelZrYA+DeSsbAj6SHPduBAhjyfInnzapInx77+l+TESn+Ss+GTgCeB\neSTjq7tD+mSS8bGLgddJ/jH1T9tWSJ5s5eRKL6T8fPJcF16/JeXtv5Dk6zkkPToAI+kt/zTsd2XK\n67u3FZInWznd6YXkyVZ+Pvv6KfABdz8BwMw+QfK5fxj4HHCLuy82s6tD+vdJvs6PcvdTQp7Ubdny\npKf/TSgj332l1quQ8rOV8wng2pDnBpLhkX82s9tJhhm+S9ILf9rdPxLSDwAPkXSYPksyM+XNtG2F\n5CmmnErkeZzkhO1qkpk133H334TjtDfkfxG4L2zrNLN7SP6Wjif51jck7H8qyZDRHPJVSPQv9kHy\n1WZIWG4Jjf10SF+fnh7WD9Zjnjz21S+8Mb/jaG9gA8mYX3r64CrlqVb5z5J8zTyfZGzyfJLxyS0k\nY4vp6R8M275dYJ5fVSFPMXX+ILAl5XO/iqNf+ddz9MRbavrbOfakZD55yrmvcud5ISVP6gnK1JOy\nR9LD+qGU5V6dh+RvYB3JP707SaYzPkoyRLWepJOUvm07yTmA/iQzYfqFfRnwXCGxtloXFh3n7vsA\n3H0ryR/Ah4CRJP/Njkk3s6+EtLrLk2Nf7u6H3f0A8KK7/y60/w2SM/nHpLv7wWrkqWL5E0l6Kv9A\nMkXsSZLgP4ZkDPKYdHf/Wdi2psA876l0nmLqHLYdMLMTwgUr/dy9k6M8Pd3d9wMUkqec+6pAnl+S\nXF8CsN7M2sLyVpJ54cekm9kZ4Zj9VQx5gIHAG+7+mLvPI5n++Q2S4dhWd38rw7buE7pDSTpJ3ft9\nGzCAQhQS/Yt9kHwVHZ+W1p+kV3M4Q/rdJEGzHvPk2tfxIe24lO2rCVOc0tKHkXwFq3SeapU/jGQK\nWPfsgyUcO9UrY3pP22qdp9B9kfyhv0Qyw+IlYGRI30YyRzo9fQjJEFYhecq5r3LneSfJicAXgV+Q\ndAxeIjlxuDRD+s+A9wN3RZLnNeDsLHFwXZb0z4TjuI1knH0F8F8k/xxvKijWlhqs8yok+eC/I0v6\nh7PkmVmPeXLs6/ws6e8kZb5sSvqJwPuqkKda5Z/IsfOCLwf+OcPrMqbXc55i9pX2muOB0fmmF5On\nnPsqNQ9JL/1skm9tqVP4Mqb3tK035SHMdslybHra9k6OXsw0nGRmYMZZQT09dD90EZFIRHFzLhER\nUUAXEYmGArr0GWY208zczN5T67qIVIICuvQls0lmJ8yudUVEKkEBXfoEMxtCcnn6PODKkHacmX3D\nzF4ws+Vm9iMzuyJsm2hmPzOzNWb2EzMbWcPqi+RFAV36ihnAo+7+K6DLzCYCf0ZytW8r8DFgCoCZ\nDQC+TnLv64nAN4FbalFpkUL0r3UFRKpkNsmd8gDuD+v9Se6n8Rawy8yeCNvHAGcBy80Mktsc7Kxu\ndUUKp4Au0TOzBpKbZ40zMycJ0E5yM6mMWYCN7j6lSlUUKQsNuUhfcAXJrW9PdfcWT+4S+DLJJeof\nDWPpTST35YHkV2cazezIEIyZja1FxUUKoYAufcFs/rA3/l2SHz3oIPmhgm+T3Idmr7u/TvJP4Mtm\ntp7k7nl/Ur3qihRHl/5Ln2ZmQ9x9X7hr4Erg/e6+q9b1EimGxtClr3vEzIaT3Pb0nxTMpTdTD11E\nJBIaQxcRiYQCuohIJBTQRUQioYAuIhIJBXQRkUgooIuIROL/AS/dAg4yRBCfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685c92fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age', 'Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发病率跟年龄也有一定的相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1685b7eb780>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IPmX/O7Bo1jJq1K3qFHPu7Hn27dxPbGzivwO5/LypWrsSMyc/+q/3OylYujAR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685a32bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "热力图可以看出有些参数是有一些相关性的，可以加入正则项进行校正"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
